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Inertial navigation system

About: Inertial navigation system is a research topic. Over the lifetime, 14582 publications have been published within this topic receiving 190618 citations. The topic is also known as: intertial guidance system & inertial reference platform.


Papers
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Journal ArticleDOI
TL;DR: This paper describes an algorithm, based on the unscented Kalman filter, for self-calibration of the transform between a camera and an inertial measurement unit (IMU), which demonstrates accurate estimation of both the calibration parameters and the local scene structure.
Abstract: Visual and inertial sensors, in combination, are able to provide accurate motion estimates and are well suited for use in many robot navigation tasks. However, correct data fusion, and hence overall performance, depends on careful calibration of the rigid body transform between the sensors. Obtaining this calibration information is typically difficult and time-consuming, and normally requires additional equipment. In this paper we describe an algorithm, based on the unscented Kalman filter, for self-calibration of the transform between a camera and an inertial measurement unit (IMU). Our formulation rests on a differential geometric analysis of the observability of the camera—IMU system; this analysis shows that the sensor-to-sensor transform, the IMU gyroscope and accelerometer biases, the local gravity vector, and the metric scene structure can be recovered from camera and IMU measurements alone. While calibrating the transform we simultaneously localize the IMU and build a map of the surroundings, all without additional hardware or prior knowledge about the environment in which a robot is operating. We present results from simulation studies and from experiments with a monocular camera and a low-cost IMU, which demonstrate accurate estimation of both the calibration parameters and the local scene structure.

555 citations

Proceedings ArticleDOI
13 Oct 2009
TL;DR: This paper uses low-performance Micro-Electro-Mechanical inertial sensors attached to the foot of a person, and describes, implements and compares several of the most relevant algorithms for step detection, stride length, heading and position estimation.
Abstract: Human localization is a very valuable information for smart environments. State-of-the-art Local Positioning Systems (LPS) require a complex sensor-network infrastructure to locate with enough accuracy and coverage. Alternatively, Inertial Measuring Units (IMU) can be used to estimate the movement of a person, by detecting steps, estimating stride lengths and the directions of motion; a methodology that is called Pedestrian Dead-Reckoning (PDR). In this paper, we use low-performance Micro-Electro-Mechanical (MEMS) inertial sensors attached to the foot of a person. This sensor has triaxial orthogonal accelerometers, gyroscopes and magnetometers. We describe, implement and compare several of the most relevant algorithms for step detection, stride length, heading and position estimation. The challenge using MEMS is to provide location estimations with enough accuracy and a limited drift. Several tests were conducted outdoors and indoors, and we found that the stride length estimation errors were about 1%. The positioning errors were almost always below 5% of the total travelled distance. The main source of positioning errors are the absolute orientation estimation.

551 citations

Book
24 Nov 2000
TL;DR: In this article, the global positioning system (GPS) geodetic application is considered and an initialization and alignment of the GPS system is described in terms of the inertial measurement unit (IMU).
Abstract: Coordinate frames and transformations ordinary differential equations inertial measurement unit inertial navigation system system error dynamics stochastic processes and error models linear estimation INS initialization and alignment the global positioning system (GPS) geodetic application.

538 citations

Proceedings ArticleDOI
27 Jun 2007
TL;DR: This work proposes a technique for high-accuracy localization of moving vehicles that utilizes maps of urban environments that integrates GPS, IMU, wheel odometry, and LIDAR data acquired by an instrumented vehicle, to generate high-resolution environment maps.
Abstract: Many urban navigation applications (e.g., autonomous navigation, driver assistance systems) can benefit greatly from localization with centimeter accuracy. Yet such accuracy cannot be achieved reliably with GPS-based inertial guidance systems, specifically in urban settings. We propose a technique for high-accuracy localization of moving vehicles that utilizes maps of urban environments. Our approach integrates GPS, IMU, wheel odometry, and LIDAR data acquired by an instrumented vehicle, to generate high-resolution environment maps. Offline relaxation techniques similar to recent SLAM methods [2, 10, 13, 14, 21, 30] are employed to bring the map into alignment at intersections and other regions of self-overlap. By reducing the final map to the flat road surface, imprints of other vehicles are removed. The result is a 2-D surface image of ground reflectivity in the infrared spectrum with 5cm pixel resolution. To localize a moving vehicle relative to these maps, we present a particle filter method for correlating LIDAR measurements with this map. As we show by experimentation, the resulting relative accuracies exceed that of conventional GPS-IMU-odometry-based methods by more than an order of magnitude. Specifically, we show that our algorithm is effective in urban environments, achieving reliable real-time localization with accuracy in the 10centimeter range. Experimental results are provided for localization in GPS-denied environments, during bad weather, and in dense traffic. The proposed approach has been used successfully for steering a car through narrow, dynamic urban roads.

532 citations

Journal ArticleDOI
TL;DR: A survey of the information sources and information fusion technologies used in current in-car navigation systems is presented and the pros and cons of the four commonly used information sources are described.
Abstract: In-car positioning and navigation has been a killer application for Global Positioning System (GPS) receivers, and a variety of electronics for consumers and professionals have been launched on a large scale. Positioning technologies based on stand-alone GPS receivers are vulnerable and, thus, have to be supported by additional information sources to obtain the desired accuracy, integrity, availability, and continuity of service. A survey of the information sources and information fusion technologies used in current in-car navigation systems is presented. The pros and cons of the four commonly used information sources, namely, 1) receivers for radio-based positioning using satellites, 2) vehicle motion sensors, 3) vehicle models, and 4) digital map information, are described. Common filters to combine the information from the various sources are discussed. The expansion of the number of satellites and the number of satellite systems, with their usage of available radio spectrum, is an enabler for further development, in combination with the rapid development of microelectromechanical inertial sensors and refined digital maps.

524 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023309
2022657
2021491
2020889
20191,003
20181,013